Ana G Rappold | Searcharxiv

arXiv2025

A Spectral Confounder Adjustment for Spatial Regression with Multiple Exposures and Outcomes

Characterizing social vulnerability is fundamental to disaster response planning. Numerous vulnerability indicators have been developed, but they are typically not validated for their predictive power over the outcomes of interest. As with many environmental health studies where interventions are impractical or unethical, validation of social vulnerability against public health outcomes is observational and relies on spatially-dependent data. Observational studies are susceptible to bias induced by unmeasured confounders. This problem is exacerbated in spatial studies with multiple health outcomes and environmental exposure variables, as the source and magnitude of confounding bias may differ by spatial scale and across exposure/outcome pairs. We propose to mitigate confounding effects in multivariate spatial studies using a tensor-regression model that allows exposure effects to vary by exposure, outcome, and spatial scale. By extracting exposure effects that correspond to local spatial scales, we replace the strict causal assumption of no unmeasured confounders with a more realistic assumption of local unconfoundedness i.e., differences between nearby regions are unconfounded, allowing for causal interpretation. Our study of the Southern United States shows that economic resilience (Theme 1) demonstrates the strongest positive effect on diabetes and negative effect on hyperlipidemia. Our analysis reveals a concise representation of the effects of social vulnerability indices on an array of chronic health outcomes, offering interpretable epidemiological insights that adjust for multivariate spatial confounding.

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arXiv2020

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided.

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